Stability analysis and breast tumor classification from 2D ARMA models of ultrasound images

Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009

Volumn , Issue , 2009, Pages 3763-3766

  Abdulsadda, A ^a   Bouaynaya, N ^a   Iqbal, K ^a  

^a University of Arkansas at Little Rock ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP NEURAL NETWORKS; IMAGE CLASSIFICATION; NEURAL NETWORKS; STABILITY; TUMORS; ULTRASONICS;

AUTOREGRESSIVE MOVING AVERAGE; BREAST TUMOR CLASSIFICATIONS; CLASSIFICATION ACCURACY; MULTI LAYER PERCEPTRON; STABILITY ANALYSIS; STABILITY PROPERTIES; STATISTICAL INFERENCE; TWO DIMENSIONAL (2 D);

MEDICAL IMAGING;

EID: 77951008482     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2009.5334482     Document Type: Conference Paper

References (11)

1. N. Bouaynaya, J. Zielinski, and D. Schonfeld, "Two-dimensional ARMA modeling for breast cancer detection and classification," in arXiv:0906.3722v1, June 2009.
2. D. R. Chen, R. F. Chang, and Y. L. Huang, "Computer-aided diagnosis applied to US of solid breast nodules by using neural networks," Radiology, vol.213, pp. 407-412, 1999.
3. C. Hansen, N. Httebruker, M. Hollenhorst, A. Schasse, L. Heuser, G. Schulte-Altedorneburg, and H. Ermert, "An automated system for full angle spatial compounding in ultrasound breast imaging," in European Conference of the International Federation for Medical and Biological Engineering, vol.22, February 2009, pp. 541-545.
4. E. I. Jury and M. Mansour, "A note on new inner-matrix for stability," proceedings of the IEEE, vol.69, pp. 1579-1580, December 1981.
5. A. Kanellakis, S. Tzafestas, and N. Theodorou, "Stability tests for 2D system using schwarz form and the inners determinants," IEEE Transaction on circuits and systems, vol.38, no.9, pp. 1071-1077, September 1991.
6. C. K. Kuhl, S. Schrading, C. C. Leutner, N. Morakkabati-Spitz, E. Wardelmann, R. Fimmers, W. Kuhn, and H. H. Schild, "Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer," Journal of Clinical Oncology, vol.23, no.33, pp. 8469-8476, November 2005.
7. Z. Ling, L. Jiangli, L. Deyu, W. Tianfu, P. Yulan, and L. Yan, "Classification of breast tumors on ultrasound images using a hybrid neural network," in The International Conference on Bioinformatics and Biomedical Engineering, July 2007, pp. 574-576.
8. C. Ruggiero, F. Bagnoli, R. Sacile, M. Calabrese, G. Rescinito, and F. Sardanelli, "Automatic recognition of malignant lesions in ultrasound images by artificial neural networks," in IEEE International Conference of Engineering in Medicine and Biology Society, October 1998, pp. 872-875.
9. S. Samarasinghe, Neural Networks for Applied Science and Engineering. Taylor & Francis Group, LLC, 2007.
10. D. Siljak, "Stability criteria for two variable polynomials," IEEE Transaction on circuits and systems, vol.22, pp. 185-189, March 1975.
11. S. Woo, Y. S. Yang, W. K. Moon, and H. C. Kim, "Computer-aided diagnosis of solid breast nodules: Use of an artificial neural network based on multiple sonographic features," IEEE Transactions on Medical Imaging, vol.23, no.10, pp. 1397-1400, Ocotber 2004.